"""resnet in pytorch
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
    Deep Residual Learning for Image Recognition
    https://arxiv.org/abs/1512.03385v1
Code Credit: https://raw.githubusercontent.com/weiaicunzai/pytorch-cifar100/master/models/resnet.py
"""

import torch
import torch.nn as nn
import copy

class BasicBlock(nn.Module):
    """Basic Block for resnet 18 and resnet 34
    """

    #BasicBlock and BottleNeck block
    #have different output size
    #we use class attribute expansion
    #to distinct
    expansion = 1

    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()

        #residual function
        self.residual_function = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(out_channels * BasicBlock.expansion)
        )

        #shortcut
        self.shortcut = nn.Sequential()

        #the shortcut output dimension is not the same with residual function
        #use 1*1 convolution to match the dimension
        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels * BasicBlock.expansion)
            )

    def forward(self, x):
        return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))

class BottleNeck(nn.Module):
    """Residual block for resnet over 50 layers
    """
    expansion = 4
    def __init__(self, in_channels, out_channels, stride=1, btn_config=None):
        super().__init__()
        if btn_config:
            # e.g., [[64, 64, 256], 256] or [64, 64, 256]
            if isinstance(btn_config[0], list):
                channels = btn_config[0]
                transit_channel = btn_config[1]
            else:
                channels = btn_config
            out_channels = None # no use
            if channels[0] == 0 or channels[1] == 0:
#                self.residual_function = nn.Sequential()
                # dummy
                self.residual_function = nn.Sequential(
                    nn.Conv2d(in_channels, 1, kernel_size=1, bias=False),
                    nn.BatchNorm2d(1),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(1, 1, stride=stride, kernel_size=3, padding=1, bias=False),
                    nn.BatchNorm2d(1),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(1, channels[2], kernel_size=1, bias=False),
                    nn.BatchNorm2d(channels[2]),
                )


            else:
                self.residual_function = nn.Sequential(
                    nn.Conv2d(in_channels, channels[0], kernel_size=1, bias=False),
                    nn.BatchNorm2d(channels[0]),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(channels[0], channels[1], stride=stride, kernel_size=3, padding=1, bias=False),
                    nn.BatchNorm2d(channels[1]),
                    nn.ReLU(inplace=True),
                    nn.Conv2d(channels[1], channels[2], kernel_size=1, bias=False),
                    nn.BatchNorm2d(channels[2]),
                )

            self.shortcut = nn.Sequential()

            if stride != 1 or in_channels != channels[2]:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_channels, transit_channel, stride=stride, kernel_size=1, bias=False),
                    nn.BatchNorm2d(transit_channel)
                )
        else:
            self.residual_function = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(inplace=True),
                nn.Conv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),
                nn.BatchNorm2d(out_channels * BottleNeck.expansion),
            )

            self.shortcut = nn.Sequential()

            if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),
                    nn.BatchNorm2d(out_channels * BottleNeck.expansion)
                )

    def forward(self, x):

        if self.residual_function[0].out_channels == 1: # dummy
            return nn.ReLU(inplace=True)(self.shortcut(x))
        else:
            return nn.ReLU(inplace=True)(self.residual_function(x) + self.shortcut(x))

class ResNet(nn.Module):

    def __init__(self, block, num_block, num_classes=10, cfg=None):
        super().__init__()

        self.num_block = num_block
        self.config = cfg

        if self.config is None:
            self.in_channels = 64

            self.conv1 = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
                nn.BatchNorm2d(64),
                nn.ReLU(inplace=True))
            #we use a different inputsize than the original paper
            #so conv2_x's stride is 1
            self.conv2_x = self._make_layer(block, 64, num_block[0], 1)
            self.conv3_x = self._make_layer(block, 128, num_block[1], 2)
            self.conv4_x = self._make_layer(block, 256, num_block[2], 2)
            self.conv5_x = self._make_layer(block, 512, num_block[3], 2)
            self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
            self.fc = nn.Linear(512 * block.expansion, num_classes)
        else:
            assert len(num_block) + 1 == len(self.config) # first conv + 4 blocks + final linear
            for i in range(len(num_block)):
                assert num_block[i] == len(self.config[i+1])
            # config = [64, [], [], [], []]; For each internal [], we have [[[64, 64, 256], 256], [64, 64, 256], [64, 64, 256]]
            self.in_channels = self.config[0]

            self.conv1 = nn.Sequential(
                nn.Conv2d(3, self.in_channels, kernel_size=3, padding=1, bias=False),
                nn.BatchNorm2d(self.in_channels),
                nn.ReLU(inplace=True))
            #we use a different inputsize than the original paper
            #so conv2_x's stride is 1
            self.conv2_x = self._make_layer(block, self.config[1][-1][-1], num_block[0], 1, layer_id=0)
            self.conv3_x = self._make_layer(block, self.config[2][-1][-1], num_block[1], 2, layer_id=1)
            self.conv4_x = self._make_layer(block, self.config[3][-1][-1], num_block[2], 2, layer_id=2)
            self.conv5_x = self._make_layer(block, self.config[4][-1][-1], num_block[3], 2, layer_id=3)
            self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
            self.fc = nn.Linear(self.config[4][-1][-1], num_classes)



    def _make_layer(self, block, out_channels, num_blocks, stride, layer_id=-1):
        """make resnet layers(by layer i didnt mean this 'layer' was the
        same as a neuron netowork layer, ex. conv layer), one layer may
        contain more than one residual block
        Args:
            block: block type, basic block or bottle neck block
            out_channels: output depth channel number of this layer
            num_blocks: how many blocks per layer
            stride: the stride of the first block of this layer
        Return:
            return a resnet layer
        """

        # we have num_block blocks per layer, the first block
        # could be 1 or 2, other blocks would always be 1
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for idx, stride in enumerate(strides):
            if self.config:
                layers.append(block(self.in_channels, out_channels, stride, btn_config=self.config[layer_id + 1][idx]))
                self.in_channels = self.config[layer_id + 1][idx][-1]
            else:
                layers.append(block(self.in_channels, out_channels, stride))
                self.in_channels = out_channels * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        output = self.conv1(x)
        output = self.conv2_x(output)
        output = self.conv3_x(output)
        output = self.conv4_x(output)
        output = self.conv5_x(output)
        output = self.avg_pool(output)
        output = output.view(output.size(0), -1)
        output = self.fc(output)

        return output

def resnet50_cifar10(cfg=None):
    """ return a ResNet 50 object
    """
    return ResNet(BottleNeck, [3, 4, 6, 3], cfg=cfg)

def resnet18_cifar10(cfg=None):
    return ResNet(BasicBlock, [2, 2, 2, 2], cfg=cfg)
